Chatbot and LLM Testing Checklist for US Product Teams
2026-09-13 · 8 min
A practical chatbot and LLM testing checklist: golden sets, hallucinations, jailbreaks, RAG, tools, and release gates — separate from classic QA retainers. Start a free QA audit or contact TestSync Lab.
Treat the model as a risky dependency, not a magic feature
Chatbots and LLM features fail differently from CRUD apps: fluent wrong answers, prompt injection, retrieval misses, tool-call chaos, and quiet regressions when a prompt or model version changes. A classic smoke suite will not catch “confidently wrong refund policy.”
US teams shipping copilots need an explicit AI test pack beside product QA. Keep scopes separate so a $999 functional retainer is not asked to absorb open-ended model research.
Checklist: before you call it ready
Golden set: 30–100 prompts with expected behaviors (not always exact strings). Hallucination checks on policy, pricing, and “I don’t know” paths. Jailbreak and prompt-injection attempts. PII leakage probes. Latency and timeout behavior. Refusal quality for disallowed topics. Multilingual or tone checks if you sell them.
If you use RAG: citation presence, retrieval of the right doc, behavior when docs conflict, and empty-index behavior. If you use tools/agents: allowed tool only, argument validation, and failure when the tool errors. Log redaction in traces.
Release gates that survive model swaps
Fail the release if golden-set critical cases regress, if jailbreak severity rises, or if retrieval smoke fails on a known doc. Version prompts and models in the report. Re-run a thin AI smoke on every prompt/model change — the same way you re-run checkout smoke on a payments library bump.
Human review still matters for tone and brand. Automation scores the pack; humans sample failures and edge UX.
How TestSync Lab scopes AI vs product QA
Product QA retainers cover app functionality from $999. AI testing is a scoped sprint or add-on with a written surface: chatbot, RAG, evals, or workflows. We do not bury unbounded LLM work inside Basic.
Open /contact for a free QA audit on classic product risk, or select an AI Testing Sprint when the model surface is the risk. We will recommend one, both, or neither.